A method for operating at least one automated vehicle, including the steps: detecting road users by sensors with the aid of the at least one automated vehicle and/or with the aid of sensor systems in an infrastructure; ascertaining predicted traffic routes for the road users with the aid of a computing device based on defined criteria; transmitting control data corresponding to the predicted traffic route to the automated vehicle; and operating the automated vehicle according to the control data.

A vehicle includes a cabin having a first room and a second room that are capable of accommodating at least one passenger, and configured to isolate one or more passengers accommodated in the first room from one or more passengers accommodated in the second room, a guidance apparatus configured to guide the at least one passenger to be accommodated in either the first room or the second room, and a control apparatus configured to control the guidance apparatus. When a user boards as the at least one passenger, the control apparatus determines which of the first room and the second room the user is to board, based on information regarding the user. The guidance apparatus is configured to guide and board the user to whichever of the first room and the second room determined by the control apparatus.

A vehicle control apparatus comprises a first detection unit configured to have a first detection range, a second detection unit configured to have a second detection range which at least partially overlaps the first detection range, and a vehicle control unit configured to be capable of performing vehicle control based on a first control state and vehicle control based on a second control state which has a high vehicle control automation rate or a reduced degree of vehicle operation participation requested to a driver compared to the first control state. The vehicle control unit performs control to shift from the first control state to the second control state based on a condition that a match degree between pieces of preceding object information of a vehicle detected by the first detection unit and the second detection unit.

Detecting out-of-model scenarios for an autonomous vehicle including: determining, based on first sensor data from one or more sensors, an environmental state relative to the autonomous vehicle, wherein operational commands for the autonomous vehicle are based on a selected machine learning model, wherein the selected machine learning model comprises a first machine learning model; comparing the environmental state to a predicted environmental state relative to the autonomous vehicle; and determining, based on a differential between the environmental state and the predicted environmental state, whether to select a second machine learning model as the selected machine learning model.

The present disclosure relates to a vehicle and control method thereof, to a vehicle having a driver assistance system for assisting a driver. When a lane change is requested even though it does not meet the predetermined lane change condition, present disclosure provides a vehicle driver assistance system (ADAS) that can actively indicate a lane change intention to an adjacent vehicle through ‘deflected driving in a lane’ and perform lane change safely after confirming the yield/overtake intention of the adjacent vehicle. It is an aspect of the present disclosure to provide a control method of a vehicle, including: confirming whether the surrounding situation of the vehicle satisfies a lane change condition when a lane change command occurs while the vehicle is driving autonomously; performing deflected driving in the lane of the vehicle to indicate a lane change intention when the surrounding situation of the vehicle does not satisfy the lane change condition; and performing a lane change corresponding to the lane change command when the yield intention for the lane change intention is confirmed from another vehicle around the traveling lane after indicating the lane change intention through the deflected driving.

An autonomous driving control apparatus installable in a vehicle includes a path determining section, an obstacle determining section that determines whether an obstacle on the planned driving path is a passage acceptable obstacle or a passage unacceptable obstacle, the passage acceptable obstacle being previously set as an obstacle that the vehicle is allowed to come into contact with while passing, the passage unacceptable obstacle being previously set as an obstacle that the vehicle is not allowed to come into contact with while passing, and a control instructing section that gives an instruction of control to a maneuver controller to perform at least one of controlling a speed of the vehicle and controlling a steering of the vehicle to control a maneuver of the vehicle. If the obstacle is determined to be the passage acceptable obstacle, the control instructing section gives an instruction of the control to pass over the obstacle.

The technology relates to cargo vehicles. National, regional and/or local regulations set requirements for operating cargo vehicles, including how to distribute and secure cargo, and how often the cargo should be inspected during a trip. However, such regulations have been focused on traditional human-driven vehicles. Aspects of the technology address various issues involved with securement and inspection of cargo before a trip, as well as monitoring during the trip so that corrective action may be taken as warranted. For instance, imagery and other sensor information may be used to enable proper securement of cargo before starting a trip. Onboard sensors along the vehicle monitor the cargo and securement devices/systems during the trip to identify issues as they arise. Such information is used by the onboard autonomous driving system (or a human driver) to take corrective action depending on the nature of the issue.

A system, method, and computer-readable medium having instructions stored thereon to enable an ego vehicle having an autonomous driving function to estimate and traverse a curved segment of highway utilizing radar sensor data. The radar sensor data may comprise stationary reflections and moving reflections. The ego vehicle may utilize other data, such as global positioning system data, for the estimation and traversal. The estimation of the curvature may be refined based upon a lookup table or a deep neural network.

Methods and systems for controlling an autonomous vehicle. The method includes receiving sensor data from a plurality of sensors, determining, a plurality of probability hypotheses based upon the sensor data, and receiving metadata from at least one sensor of the plurality of sensors. An integrity level of at least one of the plurality of probability hypotheses is determined based upon the received metadata and at least one action is determined based upon the determined integrity level and at least one probability hypothesis of the plurality of probability hypotheses. The at least one action is then initiated by an electronic controller for the vehicle.

A method for determining information related to a lane change of a target vehicle includes obtaining information related to an environment of the target vehicle. The information related to the environment relates to a plurality of features of the environment of the target vehicle. The plurality of features are partitioned into two or more groups of features. The method further determines two or more weighting factors for the two or more groups of features. An attention mechanism is used for determining the two or more weighting factors. The method further determines the information related to the lane change of the target vehicle based on the information related to the environment of the target vehicle using a machine-learning network. A weighting of the plurality of features of the environment of the target vehicle within the machine-learning network is based on the two or more weighting factors for the two or more groups of features.